Intel announced the launch of its second generation deep learning processors, which they claim surpass Nvidia's A100.
AI, ML, and deep learning have distinct purposes and capabilities and mostly work as holistic units to deliver results and accomplish tasks.
The Python programming language has been in the game for so long, and it is here to stay.
The framework, called Nobrainer, can critique its own analysis and tell scientists when it is likely to be wrong.
Besides time and cost savings, surrogate models could lead to new scientific discoveries, owing to their ability to handle large volumes of high-dimensional data.
Researchers demonstrated a technique that converts malware binary form into grayscale images, which are scanned by an image pattern recognition algorithm.
Many organizations already pay users to inform them of day-zero bugs, this new tool could save them a lot of time and money.
Previously, the way to identify these disorders would be through manual scoring, which is time-consuming and can lead to inaccuracies.
Deep learning is capable of incredible things, but if you are working with mostly structured data for straightforward purposes, Machine Learning can be a much more viable and affordable application, especially as a smaller organization with limited resources.
Popular open source tech can help future-proof your teams.